IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

Rocket-Powered Data Science

JULY 7, 2019

Computer Vision: Data Mining: Data Science: Application of scientific method to discovery from data (including Statistics, Machine Learning, data visualization, exploratory data analysis, experimentation, and more). They cannot process language inputs generally. See [link]. 4) Prosthetics.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Smart Data Collective

JUNE 4, 2021

These data models predict outcomes of new data. Data science is one of the highest-paid jobs of the 21st century. Data science needs knowledge from a variety of fields including statistics, mathematics, programming, and transforming data. Here are the chronological steps for the data science journey.

Data Science 105

Data Science Statistics Deep Learning Machine Learning 105

FineReport

JUNE 11, 2021

And do you know what the key to unlocking value from data is? This article will discuss the definition of business intelligence and analytics and the difference between them. Business analytics is how companies use statistical methods and techniques to analyze historical data to gain new insights and improve strategic decision-making.

Business Intelligence 52

Business Intelligence Analytics Dashboards Statistics 52

FineReport

DECEMBER 19, 2019

Professional data analysts must have a wealth of business knowledge in order to know from the data what has happened and what is about to happen. In addition, tools for data analysis and data mining are also important. Excel, Python, Power BI, Tableau, FineReport are frequently used by data analysts.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. It’s quite popular for its visualizations: charts, graphs, pictures, and various plots. These visualizations are useful for helping people visualize and understand trends , outliers, and patterns in data.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

FineReport

DECEMBER 22, 2019

You may have viewed many articles or reviews about reporting tools lists or open-source reporting tools. Enterprise Reporting For Visualization . As the types of charts become more diverse, and the visual effects become more impressive, traditional reporting software in the companies begins to play a role in data visualization.

Reporting 90

Reporting Software Dashboards OLAP 90

Occam's Razor

AUGUST 22, 2011

Be data driven?" " + Strategic Analysis Articles. Tactical Analysis Articles. Blogging Experience Articles. + Book Articles. Misc Articles. In each section the listing is from the latest article to the earliest. Convert Data Skeptics: Document, Educate & Pick Your Poison.

KPI 124

KPI Metrics Measurement ROI 124

datapine

MARCH 25, 2022

There is not a clear line between business intelligence and analytics, but they are extremely connected and interlaced in their approach towards resolving business issues, providing insights on past and present data, and defining future decisions. Your Chance: Want to extract the maximum potential out of your data?

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

datapine

DECEMBER 28, 2021

1) What Is A Misleading Statistic? 2) Are Statistics Reliable? 3) Misleading Statistics Examples In Real Life. 4) How Can Statistics Be Misleading. 5) How To Avoid & Identify The Misuse Of Statistics? If all this is true, what is the problem with statistics? What Is A Misleading Statistic?

Statistics 363

Statistics Advertising Visualization Data mining 363

datapine

NOVEMBER 27, 2019

Data exploded and became big. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. 2) Data Discovery/Visualization. We all gained access to the cloud.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Jet Global

MAY 1, 2023

This is in contrast to traditional BI, which extracts insight from data outside of the app. We rely on increasingly mobile technology to comb through massive amounts of data and solve high-value problems. Plus, there is an expectation that tools be visually appealing to boot. Their dashboards were visually stunning.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52